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Risk analysis of rainstorm-urban lifeline system disaster chain based on the PageRank-risk matrix and complex network

Author

Listed:
  • Hai-xiang Guo

    (China University of Geosciences
    China University of Geosciences)

  • Xin-yu He

    (China University of Geosciences
    China University of Geosciences)

  • Xin-biao Lv

    (China University of Geosciences
    China University of Geosciences)

  • Yang Wu

    (China University of Geosciences)

Abstract

Rainstorm disasters cause serious threats to people’s lives and property. Enhancing emergency response and decision-making capabilities for rainstorm disasters is necessary. In this paper, 87 rainstorm disasters worldwide were first analysed, and secondary events across 15 urban lifeline systems were summarized. Based on the obtained findings and the characteristics of rainstorm disaster evolution and complex network theory, a model of rainstorm disaster chains in urban lifeline systems was constructed, and both partial and overall analyses of this model were performed. With the use of the PageRank risk matrix method, quantitative node risk levels were calculated for different parts of the model, and complex network theory was applied to assess the overall chain risk. The results showed that the highest risk disaster chain was flood → houses submerged or collapsed → road damaged → traffic congestion or paralysis. The most important node was traffic congestion or paralysis, underlining the acute need for emergency response measures in urban traffic systems during rainstorm disasters. Overall, this research provides a crucial direction for preventing rainstorm disasters in urban lifeline systems.

Suggested Citation

  • Hai-xiang Guo & Xin-yu He & Xin-biao Lv & Yang Wu, 2024. "Risk analysis of rainstorm-urban lifeline system disaster chain based on the PageRank-risk matrix and complex network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 10583-10606, September.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:12:d:10.1007_s11069-024-06613-1
    DOI: 10.1007/s11069-024-06613-1
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    References listed on IDEAS

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    1. Liu, Kai & Wang, Ming & Zhu, Weihua & Wu, Jinshan & Yan, Xiaoyong, 2018. "Vulnerability analysis of an urban gas pipeline network considering pipeline-road dependency," International Journal of Critical Infrastructure Protection, Elsevier, vol. 23(C), pages 79-89.
    2. Zhao, Chen & Li, Nan & Fang, Dongping, 2018. "Criticality assessment of urban interdependent lifeline systems using a biased PageRank algorithm and a multilayer weighted directed network model," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 100-112.
    3. Xianhua Wu & Ji Guo, 2021. "Economic Impacts and Emergency Management of Disasters in China," Springer Books, Springer, edition 1, number 978-981-16-1319-7, April.
    4. Qiyuan Wang & Jundong Hou, 2023. "Hazard assessment of rainstorm-geohazard disaster chain based on multiple scenarios," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 589-610, August.
    5. Heracleous, Constantinos & Kolios, Panayiotis & Panayiotou, Christos G. & Ellinas, Georgios & Polycarpou, Marios M., 2017. "Hybrid systems modeling for critical infrastructures interdependency analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 89-101.
    6. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    7. Nan, Cen & Sansavini, Giovanni, 2015. "Multilayer hybrid modeling framework for the performance assessment of interdependent critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 10(C), pages 18-33.
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